Run AI in production. Audit-ready from day one.
Everything your applications run on in production, from complex AI systems to the enterprise apps around them. Deploy on AWS, Azure, GCP or your own data center.
Trusted by regulated enterprises to ship AI
In regulated industries, AI dies in production, not in the demo.
The demo ran on synthetic data. Production runs on real PII, PHI and cardholder data under GDPR, HIPAA, PCI DSS and FedRAMP. Three things become non-negotiable:
Data residency
PII, PHI, cardholder data, and the models trained on them can't go to a third-party API. GDPR, HIPAA, and PCI DSS rule out most hosted AI before you write a line.
Runtime security
Inference, scaling, failover, patching, and 24/7 monitoring don't ship with a model; in production they're a standing platform and security team. Every unpatched CVE becomes an audit finding.
Provable compliance
GDPR, HIPAA, PCI DSS, and FedRAMP all require reconstructing any decision months later: which model, which version, on what data, under whose access. Bolt-on logging won't survive a real review.
The runtime is the run half of kis.ai: your AI in production inside your own environment, with data residency, security, and provable compliance handled from day one.
The runtime your new and existing applications
run on, inside your environment.
The runtime is the platform underneath: intelligence, backend, operations and infrastructure.
Your applications run on top, both the ones you build on kis.ai and the existing systems you already run.
Intelligence, built in.
Models, evals, and guardrails, wired through one AI Gateway and AI Flow, with agents, hybrid search, and memory ready to use.
Backend, as a service.
IAM, data, content, jobs, workflows, integrations, messaging, and rules: the backend every app needs, already built.
Runs and defends itself.
Deploy, monitor, self-heal, scale, and patch, automated so operations doesn't become a second team you have to hire.
Your infrastructure, managed by AI.
Private VPC, on-prem, bare metal, or air-gapped, on your own hardware or a rented cloud GPU instance: infrastructure that runs wherever you need it to.
What running enterprise AI in a regulated network actually requires.
Build your own AI API. Your models, your data, your network.
One private API for models, retrieval, and backend, inside your environment. No hosted endpoint calls, so PII and PHI never leave your network.
Bring any model type, fine-tune and evaluate on your data.
Bring commercial or open models, fine-tune on your data without it leaving, and gate every release behind evals and guardrails.
Run fully air-gapped, with zero network egress.
Deploy on-prem or air-gapped, with no outbound network access. Data residency and zero egress turn security review into one boundary, not a per-integration audit.
A tamper-evident audit trail on every AI decision.
Every request, model, and data input is hashed into an append-only audit log, reconstructable months later for a GDPR, HIPAA, or PCI DSS audit.
Built and running in production today.
"Two developers built our multi-tenant asset platform, and it has run in our own cloud ever since."
Don’t take our word for it. Run it.
Your hardest workload, on the runtime, in your environment.
We run a paid proof of concept on your own infrastructure,
proven against the success criteria you set.
You’ll talk to our CTO and solutions engineers, not a sales team. Bring your security, risk, or compliance lead too.
Questions, answered.
On AWS, Azure, GCP, your own data center, private VPC, bare metal, or fully air-gapped. It is one Runtime you deploy wherever you choose, single-tenant, inside your perimeter. Choose an EU region or your own EU data center and data never crosses the Atlantic.
Yes. It runs fully air-gapped with zero network egress: no external calls, no outbound telemetry. Data residency and zero egress turn your security review into one boundary instead of a per-integration audit.
No. PII, PHI and cardholder data stay inside your network. Models, retrieval and backend run as one private service in your environment, with no calls to a hosted endpoint.
Every request, model version and data input is written to an append-only, cryptographically hashed audit trail. You can reconstruct which model decided what, on which data, months later: for a GDPR, HIPAA, PCI DSS or FedRAMP review, a model risk validation under SR 11-7, or an adverse-action explanation for a fair-lending exam.
Yes. The paid proof of concept runs in your environment against criteria you set, and your security, risk and compliance stakeholders are welcome on every call. We'd rather surface their requirements before production than after.
DevSecOps runs the Runtime: it deploys, monitors, scales, self-heals and patches itself. Inference, scaling and failover are handled by the platform, not by a standing platform and security team you have to staff.
The Runtime manages GPU pools on your own hardware or a rented cloud GPU instance in your account, so compute is yours: no per-token bill, predictable cost, and low, in-network latency. It scales pools up under load and back down when idle.
A release from AI Dev Studio arrives versioned and tested, carrying an SBOM and build provenance, and deploys to the Runtime unchanged. The Runtime then runs and protects it in production.
No. It runs on your infrastructure, your applications and models are yours as IP, and open-weight, commercial and fine-tuned models can be swapped without rebuilding. There is no vendor cloud to exit.